{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "    print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running tests with p =  0.25\n",
      "Mean of input:  10.000207878477502\n",
      "Mean of train-time output:  10.014059116977283\n",
      "Mean of test-time output:  10.000207878477502\n",
      "Fraction of train-time output set to zero:  0.749784\n",
      "Fraction of test-time output set to zero:  0.0\n",
      "\n",
      "Running tests with p =  0.4\n",
      "Mean of input:  10.000207878477502\n",
      "Mean of train-time output:  9.977917658761159\n",
      "Mean of test-time output:  10.000207878477502\n",
      "Fraction of train-time output set to zero:  0.600796\n",
      "Fraction of test-time output set to zero:  0.0\n",
      "\n",
      "Running tests with p =  0.7\n",
      "Mean of input:  10.000207878477502\n",
      "Mean of train-time output:  9.987811912159426\n",
      "Mean of test-time output:  10.000207878477502\n",
      "Fraction of train-time output set to zero:  0.30074\n",
      "Fraction of test-time output set to zero:  0.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(500, 500) + 10\n",
    "        \n",
    "for p in [0.25, 0.4, 0.7]:\n",
    "    out, _ = dropout_forward(x, {'mode': 'train', 'p': p})\n",
    "    out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p})\n",
    "\n",
    "    print('Running tests with p = ', p)\n",
    "    print('Mean of input: ', x.mean())\n",
    "    print('Mean of train-time output: ', out.mean())\n",
    "    print('Mean of test-time output: ', out_test.mean())\n",
    "    print('Fraction of train-time output set to zero: ', (out == 0).mean())\n",
    "    print('Fraction of test-time output set to zero: ', (out_test == 0).mean())\n",
    "    print()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx relative error:  5.44560814873387e-11\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10) + 10\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dropout_param = {'mode':'train', 'p':0.2, 'seed': 123}\n",
    "out, cache = dropout_forward(x, dropout_param)\n",
    "dx = dropout_backward(dout, cache)\n",
    "\n",
    "f = lambda _: dropout_forward(x, dropout_param)[0]\n",
    "dx_num = eval_numerical_gradient_array(f, x, dout)\n",
    "\n",
    "print('dx relative error: ', rel_error(dx, dx_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with dropout =  1\n",
      "Initial loss:  2.300479089768492\n",
      "W1 relative error: 1.03e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 4.56e-07\n",
      "b1 relative error: 4.66e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 1.69e-10\n",
      "\n",
      "Running check with dropout =  0.75\n",
      "Initial loss:  2.302371489704412\n",
      "W1 relative error: 1.85e-07\n",
      "W2 relative error: 2.15e-06\n",
      "W3 relative error: 4.56e-08\n",
      "b1 relative error: 1.16e-08\n",
      "b2 relative error: 1.82e-09\n",
      "b3 relative error: 1.48e-10\n",
      "\n",
      "Running check with dropout =  0.5\n",
      "Initial loss:  2.30427592207859\n",
      "W1 relative error: 3.11e-07\n",
      "W2 relative error: 2.48e-08\n",
      "W3 relative error: 6.43e-08\n",
      "b1 relative error: 5.37e-09\n",
      "b2 relative error: 1.91e-09\n",
      "b3 relative error: 1.85e-10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for dropout in [1, 0.75, 0.5]:\n",
    "    print('Running check with dropout = ', dropout)\n",
    "    model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            weight_scale=5e-2, dtype=np.float64,\n",
    "                            dropout=dropout, seed=123)\n",
    "\n",
    "    loss, grads = model.loss(X, y)\n",
    "    print('Initial loss: ', loss)\n",
    "\n",
    "    for name in sorted(grads):\n",
    "        f = lambda _: model.loss(X, y)[0]\n",
    "        grad_num = eval_numerical_gradient(f, model.params[name], h=1e-5)\n",
    "        print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "Iteration 1 / 125 - loss: 2.306293\n",
      "Epoch 0 / 25 - train acc: 0.240000; val_acc: 0.167000\n",
      "Epoch 1 / 25 - train acc: 0.364000; val_acc: 0.252000\n",
      "Epoch 2 / 25 - train acc: 0.482000; val_acc: 0.250000\n",
      "Epoch 3 / 25 - train acc: 0.632000; val_acc: 0.305000\n",
      "Epoch 4 / 25 - train acc: 0.706000; val_acc: 0.298000\n",
      "Epoch 5 / 25 - train acc: 0.756000; val_acc: 0.256000\n",
      "Epoch 6 / 25 - train acc: 0.784000; val_acc: 0.287000\n",
      "Epoch 7 / 25 - train acc: 0.880000; val_acc: 0.295000\n",
      "Epoch 8 / 25 - train acc: 0.918000; val_acc: 0.312000\n",
      "Epoch 9 / 25 - train acc: 0.930000; val_acc: 0.301000\n",
      "Epoch 10 / 25 - train acc: 0.960000; val_acc: 0.304000\n",
      "Epoch 11 / 25 - train acc: 0.972000; val_acc: 0.288000\n",
      "Epoch 12 / 25 - train acc: 0.956000; val_acc: 0.291000\n",
      "Epoch 13 / 25 - train acc: 0.978000; val_acc: 0.294000\n",
      "Epoch 14 / 25 - train acc: 0.972000; val_acc: 0.295000\n",
      "Epoch 15 / 25 - train acc: 0.956000; val_acc: 0.298000\n",
      "Epoch 16 / 25 - train acc: 0.964000; val_acc: 0.299000\n",
      "Epoch 17 / 25 - train acc: 0.984000; val_acc: 0.305000\n",
      "Epoch 18 / 25 - train acc: 0.996000; val_acc: 0.313000\n",
      "Epoch 19 / 25 - train acc: 0.996000; val_acc: 0.305000\n",
      "Epoch 20 / 25 - train acc: 0.992000; val_acc: 0.294000\n",
      "Iteration 101 / 125 - loss: 0.005261\n",
      "Epoch 21 / 25 - train acc: 0.982000; val_acc: 0.297000\n",
      "Epoch 22 / 25 - train acc: 0.992000; val_acc: 0.320000\n",
      "Epoch 23 / 25 - train acc: 0.972000; val_acc: 0.304000\n",
      "Epoch 24 / 25 - train acc: 0.986000; val_acc: 0.303000\n",
      "Epoch 25 / 25 - train acc: 0.996000; val_acc: 0.313000\n",
      "Epoch 25 / 25 - best_val_acc: 0.320000\n",
      "0.25\n",
      "Iteration 1 / 125 - loss: 2.314190\n",
      "Epoch 0 / 25 - train acc: 0.248000; val_acc: 0.200000\n",
      "Epoch 1 / 25 - train acc: 0.362000; val_acc: 0.270000\n",
      "Epoch 2 / 25 - train acc: 0.394000; val_acc: 0.233000\n",
      "Epoch 3 / 25 - train acc: 0.488000; val_acc: 0.300000\n",
      "Epoch 4 / 25 - train acc: 0.508000; val_acc: 0.275000\n",
      "Epoch 5 / 25 - train acc: 0.518000; val_acc: 0.288000\n",
      "Epoch 6 / 25 - train acc: 0.580000; val_acc: 0.260000\n",
      "Epoch 7 / 25 - train acc: 0.584000; val_acc: 0.294000\n",
      "Epoch 8 / 25 - train acc: 0.604000; val_acc: 0.320000\n",
      "Epoch 9 / 25 - train acc: 0.614000; val_acc: 0.268000\n",
      "Epoch 10 / 25 - train acc: 0.652000; val_acc: 0.303000\n",
      "Epoch 11 / 25 - train acc: 0.668000; val_acc: 0.298000\n",
      "Epoch 12 / 25 - train acc: 0.670000; val_acc: 0.303000\n",
      "Epoch 13 / 25 - train acc: 0.672000; val_acc: 0.304000\n",
      "Epoch 14 / 25 - train acc: 0.710000; val_acc: 0.312000\n",
      "Epoch 15 / 25 - train acc: 0.706000; val_acc: 0.319000\n",
      "Epoch 16 / 25 - train acc: 0.712000; val_acc: 0.261000\n",
      "Epoch 17 / 25 - train acc: 0.776000; val_acc: 0.326000\n",
      "Epoch 18 / 25 - train acc: 0.792000; val_acc: 0.316000\n",
      "Epoch 19 / 25 - train acc: 0.790000; val_acc: 0.314000\n",
      "Epoch 20 / 25 - train acc: 0.804000; val_acc: 0.310000\n",
      "Iteration 101 / 125 - loss: 2.939197\n",
      "Epoch 21 / 25 - train acc: 0.844000; val_acc: 0.304000\n",
      "Epoch 22 / 25 - train acc: 0.810000; val_acc: 0.302000\n",
      "Epoch 23 / 25 - train acc: 0.822000; val_acc: 0.310000\n",
      "Epoch 24 / 25 - train acc: 0.852000; val_acc: 0.313000\n",
      "Epoch 25 / 25 - train acc: 0.862000; val_acc: 0.309000\n",
      "Epoch 25 / 25 - best_val_acc: 0.326000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_train = 500\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "dropout_choices = [1, 0.25]\n",
    "\n",
    "for dropout in dropout_choices:\n",
    "    model = FullyConnectedNet([500], dropout=dropout)\n",
    "    print(dropout)\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epoch=25, batch_size=100,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 5e-4,\n",
    "                  },\n",
    "                  verbose=True, print_every=100)\n",
    "    solver.train()\n",
    "    solvers[dropout] = solver    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1239d1cac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_accs = []\n",
    "val_accs = []\n",
    "for dropout in dropout_choices:\n",
    "    solver = solvers[dropout]\n",
    "    train_accs.append(solver.train_acc_history[-1])\n",
    "    val_accs.append(solver.val_acc_history[-1])\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "for dropout in dropout_choices:\n",
    "    plt.plot(solvers[dropout].train_acc_history, '-o', label='%.2f dropout' % dropout)\n",
    "plt.title('Train accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "  \n",
    "plt.subplot(3, 1, 2)\n",
    "for dropout in dropout_choices:\n",
    "    plt.plot(solvers[dropout].val_acc_history, '-o', label='%.2f dropout' % dropout)\n",
    "plt.title('Val accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
